CN111682915B - Self-allocation method for frequency spectrum resources - Google Patents

Self-allocation method for frequency spectrum resources Download PDF

Info

Publication number
CN111682915B
CN111682915B CN202010476506.5A CN202010476506A CN111682915B CN 111682915 B CN111682915 B CN 111682915B CN 202010476506 A CN202010476506 A CN 202010476506A CN 111682915 B CN111682915 B CN 111682915B
Authority
CN
China
Prior art keywords
cognitive function
formula
user
model based
water injection
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010476506.5A
Other languages
Chinese (zh)
Other versions
CN111682915A (en
Inventor
任杰
闻映红
涂亮
付博文
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Jiaotong University
Original Assignee
Beijing Jiaotong University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Jiaotong University filed Critical Beijing Jiaotong University
Priority to CN202010476506.5A priority Critical patent/CN111682915B/en
Publication of CN111682915A publication Critical patent/CN111682915A/en
Application granted granted Critical
Publication of CN111682915B publication Critical patent/CN111682915B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/373—Predicting channel quality or other radio frequency [RF] parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel

Abstract

The invention provides a frequency spectrum resource self-allocation method, which comprises the steps of constructing a game model based on a cognitive function through a Nash game model and a Starkeberg game model; constructing a water injection model based on a cognitive function by combining the game model based on the cognitive function with a classical water injection algorithm; and solving the water injection model based on the cognitive function through distributed free iteration to realize self-optimization allocation optimization of the frequency spectrum resources. In iteration, the user can effectively improve the convergence speed of the system by using an acceleration scheme. The invention realizes the optimization of the utilization efficiency of the channel resources: the channel rate of the scheme can reach the maximum value of the theoretical spectrum utilization rate, and good system performance can be obtained under different scenes. The spectrum resources can be intelligently discovered and utilized, and the utilization efficiency of the spectrum resources is improved to the maximum extent. By utilizing the capability of predicting competition results of the cognitive function, an algorithm acceleration scheme is provided, and the operation speed can be greatly improved under the condition of not reducing the performance.

Description

Self-allocation method for frequency spectrum resources
Technical Field
The invention relates to the technical field of wireless communication, spectrum resource allocation and cognitive radio, in particular to a self-allocation method of spectrum resources.
Background
With the rapid growth of mobile communication services, wireless spectrum resources are facing a significant shortage. When the spectrum resources are in shortage, a large amount of spectrum resources are wasted, some frequency bands are crowded, and some frequency bands are often in an idle state.
Existing network architectures can be classified into centralized and distributed. The distributed network has the advantages of low time delay and low communication overhead. However, the distributed network also has the problems of system instability and low utilization rate of spectrum resources.
Disclosure of Invention
The embodiment of the invention provides a frequency spectrum resource self-allocation method which is used for improving the utilization efficiency of a user on a frequency spectrum.
In order to achieve the purpose, the invention adopts the following technical scheme.
A method for spectrum resource self-allocation, the method comprising:
constructing a game model based on a cognitive function through a Nash game model and a Starkeberg game model;
constructing a water injection model based on a cognitive function by combining the game model based on the cognitive function with a classical water injection algorithm;
obtaining a frequency spectrum resource optimization distribution result by iteratively solving the water injection model based on the cognitive function;
and obtaining a frequency spectrum resource optimization distribution result by iteratively solving the water injection model based on the cognitive function and carrying out multiple convergence operations on the water injection model based on the cognitive function in the process of iterative solution at a certain time.
Preferably, the step of constructing a cognitive function-based game model through the nash game model and the starkeberg game model includes:
by the formula
Figure BDA0002516035780000021
Denotes the received signal in k pairs of users communicating with each other, where vkFor k received noise, hkkRepresenting the channel gain, x, of the userkA transmitted signal representing a user;
by the formula
Figure BDA0002516035780000022
Representing the channel capacity of the user, where pkIs xkPower of σkIs v iskPower of pkRepresenting the utilization condition of the channel by the user;
obtaining the total channel capacity of the user by performing orthogonal division on the frequency spectrum bandwidth and based on the formula (2)
Figure BDA0002516035780000023
Figure BDA0002516035780000024
In the formula, RkSatisfies the conditions
Figure BDA0002516035780000025
Assuming that users share subchannels of N frequencies, the subchannel is obtained based on formula (3)
Figure BDA0002516035780000026
In the formula, pkPower allocation strategy, S, representing userskRepresenting the feasible policy space of the user, ck[n]Represents the sum of interference noise received by the user k on the nth sub-channel;
by making
Figure BDA0002516035780000027
And the next moment
Figure BDA0002516035780000028
Establishing a relation between the predicted values, solving a formula (5) to obtain a game model based on the cognitive function
Figure BDA0002516035780000029
Figure BDA00025160357800000210
In the formula (I), the compound is shown in the specification,
Figure BDA00025160357800000211
is a cognitive coefficient of a variable and is,
Figure BDA00025160357800000212
for the current power allocation strategy it is preferred that,
Figure BDA00025160357800000213
for the power allocation strategy of the next moment, will
Figure BDA00025160357800000214
Called cognitive function, representing the information to be obtained
Figure BDA00025160357800000215
And (4) predicting.
Preferably, assuming that the users share subchannels of N frequencies, obtaining formula (5) based on formula (3) includes:
in the OFDMA system, subchannels in which K users share N frequencies are established, and equation (5) is obtained based on equation (3).
Preferably, the step of constructing the cognitive function-based water filling model through the cognitive function-based game model and by combining a classical water filling algorithm comprises the following steps:
coefficient of cognition
Figure BDA00025160357800000216
To obtain
Figure BDA00025160357800000217
Iterative water filling algorithm and formula (7) based on Nash game model to obtain user scheme
Figure BDA00025160357800000218
Figure BDA00025160357800000219
In the formula, wkIndicating a water injection line;
according to the game model based on the cognitive function, when m is 1, only
Figure BDA00025160357800000220
Has a value of m>At 1 hour
Figure BDA00025160357800000221
To obtain
Figure BDA00025160357800000222
Substituting the formula (9) into the formula (5) and obtaining the formula through Lagrange multiplier method
Figure BDA0002516035780000031
Setting the value of the formula (10) as 0, obtaining the water injection model based on the cognitive function
Figure BDA0002516035780000032
Figure BDA0002516035780000033
In the formula
Figure BDA0002516035780000034
Is a water injection line, and is characterized in that,
Figure BDA0002516035780000035
Figure BDA0002516035780000036
the cognition factor is.
Preferably, the obtaining of the result of the spectrum resource self-optimization allocation by iteratively solving the cognitive function-based water injection model comprises:
establishing a spectrum pre-allocation scheme according to a water injection model based on a cognitive function
Figure BDA0002516035780000037
In a spectrum pre-allocation scheme
Figure BDA0002516035780000038
And (4) iterating the spectrum pre-allocation scheme to obtain a spectrum resource self-optimization allocation result.
Preferably, the process of performing multiple convergence operations on the water injection model based on the cognitive function in the process of certain iterative solution specifically includes: water injection model based on cognitive function in certain iterative solution process
Figure BDA0002516035780000039
Figure BDA00025160357800000310
And carrying out multiple convergence operations.
According to the technical scheme provided by the embodiment of the invention, the self-allocation method of the spectrum resources is suitable for a distributed network architecture, and the spectrum utilization efficiency can be improved to the maximum extent without exchanging channel information among users. Meanwhile, the convergence rate of the system is improved, and the stability of the system is ensured. The method comprises the following steps: the construction of a cognitive Nash game model provides a new cognitive Nash game model, and the model endows the user with cognitive ability and has the advantages of high efficiency and simple structure; and (3) constructing a novel water injection algorithm, namely applying the cognitive function to the traditional water injection algorithm to construct the novel water injection algorithm. And (3) combining the new game model and the novel water injection algorithm to construct a spectrum resource self-optimization allocation algorithm, and applying the cognitive Nash game model to spectrum resource allocation. The frequency spectrum utilization efficiency can be improved to the maximum extent without exchanging channel information among users and setting the solving sequence; according to the acceleration scheme of the algorithm, a user can effectively improve the overall convergence speed of the system through local multiple convergence operations, and the stability of the system is ensured.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a processing flow chart of a spectrum resource self-allocation method according to the present invention;
fig. 2 is a processing flow chart of a preferred embodiment of a spectrum resource self-allocation method provided in the present invention;
fig. 3 is a diagram of a multi-user interference model of a spectrum resource self-allocation method according to the present invention;
fig. 4 is a flowchart of an algorithm of a spectrum resource self-allocation method according to the present invention;
fig. 5 is a flowchart of a fast iteration algorithm of a spectrum resource self-allocation method according to the present invention;
fig. 6 is a performance display diagram of a spectrum resource self-allocation method according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
Referring to fig. 1 and 2, a spectrum resource self-allocation method provided by the present invention includes:
constructing a game model based on a cognitive function through a Nash game model and a Starkeberg game model;
constructing a water injection model based on a cognitive function by combining the game model based on the cognitive function with a classical water injection algorithm;
the cognitive function-based water injection model is solved through one or more iterations, and an iteration accelerating process is also provided, wherein the iterative acceleration process is used for carrying out multiple convergence operations on the cognitive function-based water injection model in a certain iterative solving process, and finally, a frequency spectrum resource self-optimization distribution result is obtained.
Further, in some preferred embodiments, the above constructing a cognitive function-based game model by using a nash game model and a starkeberg game model includes:
as shown in fig. 3, in a multi-user interference channel environment, there are k pairs of users sharing a certain spectrum segment for communication, each pair of users has a transmitting side and a receiving side, the solid line represents the communication channel between the transmitting antenna and the receiving antenna of the user, and the dotted line represents the interference channel between different users; the received signal y for user k is expressed by the following formulak，
Figure BDA0002516035780000051
In the formula, vkFor k received noise, hkkRepresenting the channel gain, x, of the userkA transmitted signal representing a user;
when the channel is a Rayleigh flat fading channel, signals and noise are subjected to zero mean Gaussian distribution through a formula
Figure BDA0002516035780000052
Denotes the channel capacity of user k, where pkIs xkPower of σkIs v iskPower of pkIndicating the channel utilization by the user, pkThe larger the power allocated to the channel by the user, when p iskWhen the channel is 0, the user stops occupying the channel;
dividing a spectrum bandwidth into an Orthogonal non-overlapping series of subcarriers by an Orthogonal Frequency Division Multiple Access (OFDMA) system; suppose that K users in the system share N frequency sub-channels, and for the nth sub-channel, hkk[n]Denotes the channel gain, h, of user kki[n]Indicating the interfering channel gain p caused by user i ≠ k for user kk[n]Represents the signal xi[n]Power of σk[n]Representing the noise vk[n]The power of (d);
the total channel capacity of k users can be obtained based on equation (2)
Figure BDA0002516035780000053
Figure BDA0002516035780000054
In the formula, since the transmission power of the actual transmitter cannot be increased infinitely, the total power R of the user transmission signal is usually limited in the modelkSatisfies the conditions
Figure BDA0002516035780000055
Figure BDA0002516035780000056
Assuming that K users share N frequency subchannels in an OFDMA system, the total transmit power of all user channels satisfies condition (4), which is obtained based on equation (3)
Figure BDA0002516035780000061
Figure BDA0002516035780000062
Namely an optimization model of the distributed system; in the formula, pkPower allocation strategy, S, representing userskRepresenting the feasible policy space of the user, ck[n]Represents the sum of interference noise received by the user k on the nth sub-channel;
to solve the optimization model (5), the optimization model can be obtained by
Figure BDA0002516035780000063
And the next moment
Figure BDA0002516035780000064
Establishing a relation between the predicted values to obtain a game model based on a cognitive function
Figure BDA0002516035780000065
Figure BDA0002516035780000066
In the formula (I), the compound is shown in the specification,
Figure BDA0002516035780000067
the values, called the cognition factors,
Figure BDA0002516035780000068
for the current power allocation strategy it is preferred that,
Figure BDA0002516035780000069
for the power allocation strategy of the next moment, will
Figure BDA00025160357800000610
Called cognitive function, representing the information to be obtained
Figure BDA00025160357800000611
And (4) predicting.
Cognitive coefficients in cognitive functions
Figure BDA00025160357800000612
The method can be freely selected, the value of the method is related to the cognitive strategy of a user, and then the optimal solution of the spectrum resource allocation can be solved through iteration of a water filling algorithm.
Furthermore, the above-mentioned game model based on cognitive function, combined with the classical waterflooding algorithm, to construct a waterflooding model based on cognitive function includes:
according to the traditional Nash game model, the cognition coefficient is set
Figure BDA00025160357800000613
To obtain
Figure BDA00025160357800000614
Figure BDA00025160357800000615
Iterative WF algorithm and formula (7) based on Nash game model to obtain user scheme
Figure BDA00025160357800000616
Figure BDA00025160357800000617
In the formula, wkRepresenting a water injection line, the value of which is related to the total power of the transmitted signal;
all users in the system continuously and repeatedly optimize the power configuration of the system, and the system finally reaches a stable Nash equilibrium state within t → ∞ under appropriate conditions.
In the cognition function-based game model, when m is 1, only
Figure BDA00025160357800000618
Has a value of m>At 1 hour
Figure BDA00025160357800000619
The game model based on the cognitive function is in the following form
Figure BDA00025160357800000620
Substituting equation (9) into equation (5) and satisfying condition (4), and obtaining by the lagrange multiplier method
Figure BDA00025160357800000621
Setting the value of the formula (10) as 0, obtaining the water injection model based on the cognitive function
Figure BDA00025160357800000622
Figure BDA00025160357800000623
In the formula
Figure BDA00025160357800000624
Is a water injection line, the value of which is related to the total power of the transmitted signal,
Figure BDA0002516035780000071
Figure BDA0002516035780000072
and (3) the cognition factor when m is 1.
Further, in practical application, different communication systems and communication service types should select communication strategies suitable for them, and cognitive functions are adjusted according to the similar scheme, so that the communication system and the communication service type can obtain the cognitive functions
Figure BDA0002516035780000073
Figure BDA0002516035780000074
The novel water injection algorithm scheme can effectively improve the convergence speed of the system, further improve the iteration speed, and solve the self-optimization scheme of frequency spectrum resource allocation through accelerated iteration of the system.
As shown in fig. 4, the step of establishing the spectrum pre-allocation scheme according to the water injection model based on the cognitive function specifically includes:
establishing a spectrum pre-allocation scheme of cognitive users in OFDMA:
Figure BDA0002516035780000075
wherein the selection is made in the following simulation results demonstration
Figure BDA0002516035780000076
And performing one or more iterations on the spectrum pre-allocation scheme to obtain a result of the spectrum resource from the optimized allocation.
It should be understood by those skilled in the art that the above-mentioned selection of the cognition factor is only an example, and the cognition factor selected by those skilled in the art according to the actual requirement, such as being applicable to the embodiment of the present invention, should also be included in the protection scope of the present invention, and is included herein by reference.
The convergence speed of the system spectrum allocation algorithm is related to the stability of the whole system. If the communication resource allocation scheme is changed rapidly, the communication stability of each communication user is reduced, and the system is not favorable for adapting to a dynamic communication environment. The convergence rate of the whole hoisting system is rapidly raised
Figure BDA0002516035780000077
The equation achieves a uniform convergence speed over the system as a whole. In order to implement this process of accelerating iterative convergence, as shown in fig. 5, several times of calculations of local convergence iterations of the user may be added in any one system iteration, so as to improve the convergence process of the entire system. This idea is the accelerated iteration scheme of the present invention. In this embodiment, in the design of the accelerated iterative convergence scheme, the user can pass through the local multiple pairs in the algorithm
Figure BDA0002516035780000078
Carrying out multiple convergence operations, thereby improving the overall convergence speed of the system and ensuring the stability of the system; FIG. 6 showsThe performance improvement result of the above accelerated convergence operation method is used in OFDMA.
In summary, the self-allocation method for spectrum resources provided by the present invention has the following characteristics:
according to the Nash game model and the Starkeberg game model, a new cognition-based Nash game model is provided. Cognitive functions reflecting competition relations among users are defined in the model, and each user participating in the cognitive Nash game can select the cognitive function of the user, so that more various game models can be designed;
applying the cognitive function to the solving process of the optimization scheme, and combining the traditional water injection algorithm to construct a novel water injection algorithm, thereby providing an effective and simple theoretical tool for the optimization scheme;
and applying the cognitive Nash game model to spectrum resource allocation. In a distributed network, the algorithm can maximally improve the spectrum utilization efficiency without exchanging channel information among users and setting the solving sequence;
the user can effectively improve the overall convergence speed of the system and ensure the stability of the system through local multiple convergence operations in the spectrum allocation strategy;
according to the Nash game model and the Starkeberg game model, the new cognition-based Nash game model solves the problems of low efficiency of the traditional Nash game and complex calculation of the Starkeberg game by endowing the cognitive prediction capability to the user, and inherits the advantages of simple structure and high efficiency of the Starkeberg game of the traditional Nash game. The model provides a theoretical basis for the research of the spectrum resource self-optimization distribution algorithm based on the new cognitive game theory;
in the new cognitive Nash game model, users can freely compete like the Nash game, and balance is solved without sequence. Meanwhile, under the condition that a plurality of users have cognitive prediction capability in the system, the users can not simply predict strategies of others, but guide other users to select favorable strategies for all people, so that each user can achieve better results, collision is avoided, and the whole system obtains higher efficiency;
cognitive functions reflecting the cognition of competition relations among users are defined in the novel cognitive Nash game model, and each user participating in the cognitive Nash game can select the cognitive function of the user, so that more various game models can be designed. Each user participating in competition realizes free and fair competition through a cognitive function, so that the maximum benefit of the system is realized;
the new water injection model provided by the invention realizes the optimization of the utilization efficiency of the channel resources: the channel rate of the scheme can reach the maximum value of the theoretical spectrum utilization rate, and good system performance can be obtained under different scenes. The frequency spectrum resources can be intelligently discovered and utilized, and the utilization efficiency of the frequency spectrum resources is improved to the maximum extent;
the algorithm scheme can be applied to various communication modes, and in practical application, proper cognitive functions are selected by combining with knowledge related to game theory so as to achieve stability of the whole system. The existence of nash equilibrium needs to be ensured in the user spectrum resource competition process;
communication overhead is reduced, and safety and privacy of users are guaranteed: the cognitive prediction capability is given to the users without depending on a large number of channel information exchange conditions among the users, the optimization of respective communication rates is realized by a single user, the power distribution scheme is not in sequence, and the method is suitable for a distributed network architecture;
can actively adapt to channel changes, and has the characteristics of dynamic, high efficiency and intelligence: the secondary users can discover the idle spectrum segment without additional overhead, and can actively avoid interference to the advanced users. The proposed scheme can be adapted to diverse, dynamic, heterogeneous wireless communication scenarios;
since the cognitive prediction ability is given to the user in the cognitive nash game, the convergence rate of the entire system can be improved by utilizing the prediction ability. In practical application, a user can improve the overall convergence speed of the system and ensure the stability of the system through local multiple convergence operations in the algorithm.
The method provided by the invention also has the following beneficial effects:
(1) the optimization of the utilization efficiency of the channel resources is realized: the channel rate of the scheme can reach the maximum value of the theoretical spectrum utilization rate, and good system performance can be obtained under different scenes. The spectrum resources can be intelligently discovered and utilized, and the utilization efficiency of the spectrum resources is improved to the maximum extent.
(2) Communication overhead is reduced, and safety and privacy of users are guaranteed: the cognitive prediction capability is given to the users without depending on a large number of channel information exchange conditions among the users, and the individual users realize the optimization of respective communication rates, so that the overall performance of the system is improved.
(3) Can actively adapt to channel changes, and has the characteristics of dynamic, high efficiency and intelligence: the secondary users can discover the idle spectrum segment without additional overhead, and can actively avoid interference to the advanced users. The proposed scheme can be adapted to diverse, dynamic, heterogeneous wireless communication scenarios.
(4) The convergence speed of the whole system is improved by utilizing the prediction capability: in practical application, a user can improve the overall convergence speed of the system through local multiple convergence operations in an algorithm, so that the stability of the system is ensured;
(5) the method is suitable for a distributed network architecture, the power distribution strategy is self-optimized without sequencing, and does not depend on a large number of channel information exchange conditions among users, so that the communication overhead is reduced, and the safety and privacy of the users are guaranteed. The cognitive prediction capability is given to the user, the channel change can be actively adapted, and the method has the characteristics of dynamic, high efficiency and intelligence. The optimization of the respective communication rates of the single users is realized, so that the utilization efficiency of the whole frequency spectrum of the system reaches the theoretical maximum value. And the user can also utilize the prediction capability to improve the overall convergence speed of the system through local multiple convergence operations, thereby ensuring the stability of the system.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (2)

1. A method for self-allocation of spectrum resources, the method comprising:
through the Nash game model and the Starkeberg game model, a game model based on a cognitive function is constructed, and the method specifically comprises the following steps:
by the formula
Figure FDA0003227149580000011
Denotes the received signal in k pairs of users communicating with each other, where vkFor k received noise, hkkRepresenting the channel gain, x, of the userkA transmitted signal representing a user;
by the formula
Figure FDA0003227149580000012
Representing the channel capacity of the user, where pkIs xkPower of σkIs v iskPower of pkRepresenting the utilization condition of the channel by the user;
obtaining the total channel capacity of the user by performing orthogonal division on the frequency spectrum bandwidth and based on the formula (2)
Figure FDA0003227149580000013
Figure FDA0003227149580000014
In the formula, RkSatisfies the conditions
Figure FDA0003227149580000015
Assuming that users share subchannels of N frequencies, the subchannel is obtained based on formula (3)
Figure FDA0003227149580000016
In the formula, pkPower allocation strategy, S, representing userskRepresenting the feasible policy space of the user, ck[n]Represents the sum of interference noise received by the user k on the nth sub-channel;
by making
Figure FDA0003227149580000017
And the next moment
Figure FDA0003227149580000018
Establishing a relation between the predicted values, solving a formula (5) to obtain a game model based on the cognitive function
Figure FDA0003227149580000019
Figure FDA00032271495800000110
In the formula (I), the compound is shown in the specification,
Figure FDA00032271495800000111
is a cognitive coefficient of a variable and is,
Figure FDA00032271495800000112
for the current power allocation strategy it is preferred that,
Figure FDA00032271495800000113
for the power allocation strategy of the next moment, will
Figure FDA00032271495800000114
Called cognitive function, representing the information to be obtained
Figure FDA00032271495800000115
Predicting;
through the game model based on the cognitive function and in combination with a classical water filling algorithm, a water filling model based on the cognitive function is constructed, and the game model based on the cognitive function specifically comprises the following steps:
coefficient of cognition
Figure FDA00032271495800000116
To obtain
Figure FDA00032271495800000117
Iterative water filling algorithm and formula (7) based on Nash game model to obtain user scheme
Figure FDA00032271495800000118
Figure FDA00032271495800000119
In the formula, wkIndicating a water injection line;
according to the game model based on the cognitive function, when m is 1, only
Figure FDA0003227149580000021
With values, m > 1
Figure FDA0003227149580000022
To obtain
Figure FDA0003227149580000023
Substituting the formula (9) into the formula (5) and obtaining the formula through Lagrange multiplier method
Figure FDA0003227149580000024
Setting the value of the formula (10) as 0, obtaining the water injection model based on the cognitive function
Figure FDA0003227149580000025
Figure FDA0003227149580000026
In the formula
Figure FDA0003227149580000027
Is a water injection line, and is characterized in that,
Figure FDA0003227149580000028
Figure FDA0003227149580000029
is a cognition factor;
the water injection model based on the cognitive function is solved through iteration, and multiple times of convergence operation are carried out on the water injection model based on the cognitive function in the process of certain iterative solution, so that a frequency spectrum resource optimization allocation result is obtained;
the step of solving the cognitive function-based water injection model through iteration to obtain a spectrum resource self-optimization distribution result comprises the following steps:
establishing a spectrum pre-allocation scheme according to a water injection model based on a cognitive function
Figure FDA00032271495800000210
In a spectrum pre-allocation scheme
Figure FDA00032271495800000211
Iterating the spectrum pre-allocation scheme to obtain a spectrum resource self-optimization allocation result;
the process of performing multiple convergence operations on the water injection model based on the cognitive function in the process of certain iterative solution specifically comprises the following steps: water injection model based on cognitive function in certain iterative solution process
Figure FDA00032271495800000212
Figure FDA00032271495800000213
And carrying out multiple convergence operations.
2. The method of claim 1, wherein the user shares subchannels of N frequencies, and obtaining formula (5) based on formula (3) comprises:
in the OFDMA system, subchannels in which K users share N frequencies are established, and equation (5) is obtained based on equation (3).
CN202010476506.5A 2020-05-29 2020-05-29 Self-allocation method for frequency spectrum resources Active CN111682915B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010476506.5A CN111682915B (en) 2020-05-29 2020-05-29 Self-allocation method for frequency spectrum resources

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010476506.5A CN111682915B (en) 2020-05-29 2020-05-29 Self-allocation method for frequency spectrum resources

Publications (2)

Publication Number Publication Date
CN111682915A CN111682915A (en) 2020-09-18
CN111682915B true CN111682915B (en) 2021-10-08

Family

ID=72453037

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010476506.5A Active CN111682915B (en) 2020-05-29 2020-05-29 Self-allocation method for frequency spectrum resources

Country Status (1)

Country Link
CN (1) CN111682915B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114267168B (en) * 2021-12-24 2023-03-21 北京航空航天大学 Formation resource allocation method applied to urban expressway environment
CN115665867B (en) * 2022-09-28 2023-09-01 广东省新一代通信与网络创新研究院 Spectrum management method and system for Internet of Vehicles

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105323052A (en) * 2015-11-18 2016-02-10 湖南大学 OFDM-based cognitive radio network resource allocation method
CN110691360A (en) * 2019-08-21 2020-01-14 西北工业大学 Joint resource allocation method based on user clustering and Starkelberg model

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8737323B2 (en) * 2009-07-28 2014-05-27 Interdigital Patent Holdings, Inc. Method and apparatus for using direct wireless links and a central controller for dynamic resource allocation
CN103796211B (en) * 2014-03-07 2017-06-06 国家电网公司 Joint Power and method for channel allocation in a kind of cognition wireless network

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105323052A (en) * 2015-11-18 2016-02-10 湖南大学 OFDM-based cognitive radio network resource allocation method
CN110691360A (en) * 2019-08-21 2020-01-14 西北工业大学 Joint resource allocation method based on user clustering and Starkelberg model

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
频谱资源自优化分配算法的研究;任杰;《中国博士学位论文全文数据库信息科技辑》;20151015(第10期);第1.4-1.6节，第3章 *

Also Published As

Publication number Publication date
CN111682915A (en) 2020-09-18

Similar Documents

Publication Publication Date Title
CN107071914B (en) Dynamic mode selection and energy distributing method in a kind of energy capture D2D network
CN112601284B (en) Downlink multi-cell OFDMA resource allocation method based on multi-agent deep reinforcement learning
CN107613556B (en) Full-duplex D2D interference management method based on power control
JP2010515371A (en) Method and apparatus for supporting SDMA transmission in an OFDM network
CN108600999B (en) FD-D2D joint optimization method based on channel allocation and power control
CN107613555A (en) Non-orthogonal multiple accesses honeycomb and terminal direct connection dense network resource management-control method
CN110121212B (en) Uplink transmission method for periodic URLLC service
CN111682915B (en) Self-allocation method for frequency spectrum resources
CN110337148B (en) Cognitive radio energy efficiency resource allocation method based on non-orthogonal multiple access
CN110769514A (en) Heterogeneous cellular network D2D communication resource allocation method and system
CN104254130A (en) Method, device and system for allocating D2D (Device-to-Device) user link and cellular user shared resources
CN104703270A (en) User access and power distribution method suitable for heterogeneous wireless cellular network
CN101174870B (en) Accidental beam shaping and transmitting method based on beam collection selection
KR102391956B1 (en) Coalitional Method for Optimization of Computing Offloading in Multiple Access Edge Computing (MEC) supporting Non-Orthogonal Multiple Access (NOMA)
CN107948109B (en) Subcarrier bit joint optimization method for compromising energy efficiency and spectral efficiency in cognitive radio
Valentin et al. Integrating multiuser dynamic ofdma into ieee 802.11 wlans-llc/mac extensions and system performance
CN106686711B (en) Power distribution method of single-antenna downlink NOMA system
CN103369684A (en) Resource scheduling method and device based on carrier aggregation
CN107018528B (en) Equivalent capacity-based wireless network virtualization method in single-cell LTE-A system
CN110177340A (en) A kind of super-intensive network resource allocation method of customer-centric
Zhang et al. Energy efficient resource allocation in millimeter-wave-based fog radio access networks
Bhattarai et al. Improved bandwidth allocation in Cognitive Radio Networks based on game theory
CN115866787A (en) Network resource allocation method integrating terminal direct transmission communication and multi-access edge calculation
CN107148078B (en) User access control method and device for hybrid full-duplex and half-duplex network
CN107172574B (en) Power distribution method for D2D user to sharing frequency spectrum with cellular user

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant